ImageNet: Difference between revisions

On 30 September 2012, a [[convolutional neural network]] (CNN) called [[AlexNet]]<ref name=":0">{{Cite journal|lastlast1=Krizhevsky|firstfirst1=Alex|last2=Sutskever|first2=Ilya|last3=Hinton|first3=Geoffrey E.|accessdate=24 May 2017|title=ImageNet classification with deep convolutional neural networks|url=https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf|journal=Communications of the ACM|volume=60|issue=6|date=June 2017|pages=84–90|doi=10.1145/3065386|s2cid=195908774|issn=0001-0782}}</ref> achieved a top-5 error of 15.3% in the ImageNet 2012 Challenge, more than 10.8 percentage points lower than that of the runner up. This was made feasible due to the use of [[Graphics processing unit]]s (GPUs) during training,<ref name=":0" /> an essential ingredient of the [[deep learning]] revolution. According to ''[[The Economist]]'', "Suddenly people started to pay attention, not just within the AI community but across the technology industry as a whole."<ref name=economist/><ref>{{cite news|title=Machines 'beat humans' for a growing number of tasks|url=https://www.ft.com/content/4cc048f6-d5f4-11e7-a303-9060cb1e5f44|accessdate=3 February 2018|work=Financial Times|date=30 November 2017}}</ref><ref>{{Cite web|url=https://qz.com/1307091/the-inside-story-of-how-ai-got-good-enough-to-dominate-silicon-valley/|title=The inside story of how AI got good enough to dominate Silicon Valley|lastlast1=Gershgorn|firstfirst1=Dave|last2=Gershgorn|first2=Dave|website=Quartz|access-date=10 December 2018}}</ref>

In 2015, AlexNet was outperformed by Microsoft's very deep CNN with over 100 layers, which won the ImageNet 2015 contest.<ref name="microsoft2015">{{cite journal|last1=He|first1=Kaiming|last2=Zhang|first2=Xiangyu|last3=Ren|first3=Shaoqing|last4=Sun|first4=Jian|title=Deep Residual Learning for Image Recognition.|journal= 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)|pages=770–778|year=2016|doi=10.1109/CVPR.2016.90|arxiv=1512.03385|isbn=978-1-4673-8851-1|s2cid=206594692}}</ref>

A study of the history of the multiple layers ([[Taxonomy (general)|taxonomy]], object classes and labeling) of ImageNet and WordNet in 2019 described how [[Algorithmic bias|bias]] is deeply embedded in most classification approaches for of all sorts of images.<ref>{{Cite news|url=https://www.wired.com/story/viral-app-labels-you-isnt-what-you-think/|title=The Viral App That Labels You Isn't Quite What You Think|work=Wired|access-date=22 September 2019|issn=1059-1028}}</ref><ref>{{Cite news|url=https://www.theguardian.com/technology/2019/sep/17/imagenet-roulette-asian-racist-slur-selfie|title=The viral selfie app ImageNet Roulette seemed fun – until it called me a racist slur|last=Wong|first=Julia Carrie|date=18 September 2019|work=The Guardian|access-date=22 September 2019|issn=0261-3077}}</ref><ref>{{Cite web|url=https://www.excavating.ai/|title=Excavating AI: The Politics of Training Sets for Machine Learning|lastlast1=Crawford|firstfirst1=Kate|last2=Paglen|first2=Trevor|date=19 September 2019|website=-|url-status=live|access-date=22 September 2019}}</ref><ref>{{Cite web|url=https://arxiv.org/abs/2009.01215arXiv|title=Excavating "Excavating AI": The Elephant in the Gallery |last=Lyons|first=Michael|date=4 September 2020|websiteclass=-cs.CY |url-statuseprint=live|access-date=82009.01215 September 2020}}</ref> ImageNet is working to address various sources of bias.<ref>{{Cite web|url=http://image-net.org/update-sep-17-2019.php|title=Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy|date=17 September 2019|website=image-net.org|url-status=live|access-date=22 September 2019}}</ref>